Computer-implemented methods, computer readable medium and systems for a precision agriculture platform that identifies generic anomalies in crops

ABSTRACT

A computer platform implements a precision agriculture system that predicts output conditions, such as diseases, salt damage, soil problems, water leaks and generic anomalies, for orchards under analysis. The computer platform stores site and crop datasets and processed satellite image for the orchards. An orchard data learned model predicts a propensity for existence of output conditions associated with the permanent crops based on the data values for the variables of the site and crop datasets. Also, a satellite model predicts a propensity for existence of the output conditions at the orchard based on processed satellite images. A precision agriculture management model is disclosed that integrates the orchard data learned model with the satellite model to accurately predict the output conditions.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention is related to the field of data sensing,collection and modeling, and more specifically, to computer-implementedmethods, computer readable medium and systems for accumulating, managingand processing data for use in a precision agriculture system.

Art Background

The present application relates to improving outcomes in agriculture.Currently, various scientific efforts have been made to collect data andanalyze various parameters related to agriculture in an attempt toimprove outcomes. For example, one such parameter investigates theamount of rain water accumulated at a farm. Another such parameter maybe to look at the number of chilling hours exhibited at a particularorchard. However, these scientific efforts tend to address only a singleparameter at a time, and therefore analyze data in silos without the aidof data integration across multiple sources of available data. Anapproach, which accumulates data from multiple sources, is more robustthan the silo approach, and therefore improves the ability to computeinformation that aids in agriculture techniques.

Information technology has taken a prominent role in many industries.For example, it's difficult to imagine conducting business in thefinancial world without information technology. Information technologyalso has applications to improve agriculture too. Although the currentscientific efforts in agriculture science use analytical techniques,none take a comprehensive approach to integrate disparate datasets fromdiverse data sources. Therefore, what is needed is a comprehensivesystem that uses both state-of-the-art information technology along withall available data relevant to the cultivation of crops.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth in the appendedclaims. However, for purpose of explanation, several embodiments of theinvention are set forth in the following figures.

FIG. 1 is a diagram illustrating an exemplary precision agricultureremote sensing platform.

FIG. 2 is a block diagram illustrating a high-level flow of theprecision agriculture remote sensing platform.

FIG. 3a illustrates the content of an example of Orchard data.

FIG. 3b illustrates a block diagram to generate historical data forweather data (330) and water availability/quality data.

FIG. 3c , shows a block diagram illustrating one embodiment forsatellite image processing.

FIGS. 4a-4f depict six radar charts, one for each lot, across 4 cropyears.

FIGS. 5a-5f depict lot by various quality values.

FIGS. 6a-6b illustrate charts that depict quality items of pistachiostree data.

FIG. 7 shows a graph that depicts yield by lot for several example years

FIG. 8 illustrates one embodiment for some data structures used todefine orchard data, satellite data and weather data.

FIG. 9 is a block diagram illustrating one embodiment for evaluatingvariables in a precision agriculture system to determine outputconditions.

FIG. 10 is a block diagram illustrating a process to create the orcharddata model in accordance with one embodiment.

FIG. 11 is a flow diagram illustrating one embodiment for discoveringboundaries and variables for creating pathways or buckets in an orcharddata model.

FIG. 12 is a block diagram illustrating one embodiment for generatingthe orchard data model through a second machine learning stage.

FIGS. 13(a) and 13(b) are block diagrams illustrating one embodiment fora trained orchard data model.

FIG. 14(a) is a block diagram illustrating one embodiment to generate asatellite model for integration into a precision agriculture system.

FIG. 14(b) depicts a block diagram for processing satellite imagery toidentify characteristics through previously identified bands offrequencies discovered from the process depicted in FIG. 14(a).

FIG. 15(a) depicts using field equipment to gather bands of wavelengthsassociated with disease crops.

FIG. 15(b) depicts a process to generate healthy bands of wavelengthsreflected from healthy orchards.

FIG. 15(c) illustrates a process to collect soil bands of wavelengthsfor use in the precision agriculture system.

FIG. 15(d) illustrates a field calibration process to acquire a band offrequencies that detect water leak.

FIG. 15(e) depicts a process to generate bands of frequencies thatidentify generic anomalies in orchards.

FIG. 16 depicts a satellite image for an example orchard lot thathighlights possible Verticillium Wilt reflectance regions.

FIG. 17 depicts a satellite image for an example orchard lot thathighlights healthy vegetation reflectance regions.

FIG. 18 depicts a satellite image for an example orchard lot thathighlights possible salt damaged reflectance regions.

FIG. 19 depicts a satellite image for an example orchard lot thathighlights soil reflectance regions.

FIG. 20 illustrates a system for generating a satellite image model inaccordance with one embodiment for the precision agriculture system ofthe present invention.

FIG. 21 is a block diagram illustrating a process to identify, fromsatellite images, soil images separate from tree vegetation images.

FIG. 22 is a block diagram illustrating one embodiment for integratingan orchard data model and a satellite image model into the precisionagriculture machine learned model of the present invention.

FIG. 23 is a block diagram illustrating a precision agriculture systemthat incorporates a means to input and integrate farming knowledge.

FIG. 24 is a block diagram illustrating one embodiment for using farmingknowledge to interpret the output of the precision agriculture learnedmodel.

FIG. 25 is a flow diagram illustrating one embodiment for integratingand calibrating a precision agriculture machine learned model.

FIG. 26 is a block diagram illustrating one embodiment for a precisionagriculture system.

FIG. 27 depicts an output from the hotspot detection module identifyingintensity of generic anomalies across an example orchard.

FIG. 28 depicts hotspot analysis based on an analysis of vegetativehealth.

FIG. 29 is a flow diagram illustrating one embodiment for a run timeoperation executed by the precision agriculture system.

FIG. 30 depicts a diagrammatic representation of a machine in theexemplary form of a computer system 29A00 within which a set ofinstructions for causing the machine to perform any one of themethodologies discussed above may be executed.

DETAILED DESCRIPTION

Although the present invention has been described in terms of specificexemplary embodiments, it will be appreciated that various modificationsand alterations might be made by those skilled in the art withoutdeparting from the spirit and scope of the invention.

Precision Agriculture System

FIG. 1 is a diagram illustrating an exemplary precision agricultureremote sensing platform. In general, the platform collects data fromseveral data sources, and aggregates the data, in crop management system160, to aid in the precision management of permanent crops. For thisexemplary embodiment, the platform acquires satellite-generated data.Various satellites acquire images of Earth's terrain, which, may be usedto extract useful information regarding orchards and fields ofagriculture interest. In general, the satellite images are acquired fromreflections of light off the terrain as absorbed in several differentspectral bands or frequencies (e.g., red, green, infrared, etc.). Forexample, LANDSAT8 collects images of Earth's terrain suitable for use insome aspects of the precision agriculture system. For purposes ofnomenclature, the satellite image data is referred to herein as“satellite spectral data.” FIG. 1 illustrates exemplary satellites (180,185 and 190) that generate satellite spectral data for download tosatellite data acquisition (175). Satellite data acquisition 175 may bea satellite service provider that acquires the satellite spectral datafrom one or more satellites (e.g., Digital Globe, Pleiades, RapidEye,Landsat, etc.). Regardless of the method of data acquisition, satellitedata acquisition 175 transmits satellite spectral data to cropmanagement 160 via network 110. Network 110 may comprise any type ofpublic or private network, such as the Internet.

The precision agriculture remote sensing platform includes, for thisembodiment, external data sources, such as weather data. For theexemplary embodiment illustrated in FIG. 1, weather data acquisition(145), which consists of one or more servers/computers, acquires datafrom one or more weather stations (135 and 140) or databases that storeaggregated weather data. The weather stations (135 and 140) are locatedthroughout the regions of agriculture interest, and collect variouswell-known weather metrics, including temperature, rainfall, humidity,etc. As illustrated in FIG. 1, weather data acquisition 145 collectsweather data from the weather stations, and transmits the weather datato crop management system 160 via network 110.

In some embodiments, the precision agriculture remote sensing platformutilizes additional external data, such as soils data 150. In general,soils data provides information on types of soils of the fields andorchards of interest. For the exemplary embodiment of FIG. 1, soilsdata, stored at server/computer 150, is transmitted across network 110to crop management system 160. The origin of soils data may come fromany number of agricultural resources, including the United StatesGeological Survey (USGS).

In some embodiments, the precision agriculture remote sensing platform100 utilizes data acquired directly from the farm fields and orchards.The co-location of sensing equipment in exemplary orchards 120 isillustrated in FIG. 1. For the exemplary embodiment illustrated in FIG.1, computer/server 130 acquires data from the field through fieldequipment/data acquisition 125. For example, bands of frequencies,acquired from in-house radio spectrometer field studies, may beconducted as reference data. Data acquired from orchards 120 istransmitted to crop management system 160 through network 110. A furtherdescription of spectrometer field studies is described more fully below.

In some embodiments, the precision agriculture remote sensing platformfurther utilizes data proprietary to the orchard/farm (“orchard data”)pre-stored at the crop management system. Details regarding someembodiments of orchard data are provided below.

FIG. 2 is a block diagram illustrating a high-level flow of theprecision agriculture remote sensing platform. Satellite spectral datais acquired in satellite imagery acquisition 201. In some embodiments,satellite imagery acquisition 201 is implemented with software routines,written in R, that process the satellite images acquired from multiplesatellite sensors. The satellite imagery acquisition 201 receives, asinput, GeoTiff files and processes the image to output PNGs andshapefiles.

As shown in FIG. 2, the output of satellite imagery 201 (PNGs andshapefiles) are input to image processing and analysis 210. In general,the image processing and analysis 210 implements specialized models. Insome embodiments, the image processing and analysis 210 performsanalysis for water leak detection, spatially correlated hotspots, plantdisease, elevated soil minerals as well as crop health and severityissues. In some embodiments, the imaging processing and analysis 210 isimplemented in software operating on a general purpose computer, wherethe custom software algorithms are written in R. The image processingand analysis comprises customized statistical algorithms using bothstandard vegetative indices (derive from the satellite spectral data) aswell as proprietary wavelength calibrations (acquired from proprietaryradio spectrometer field studies). In terms of data format, the imageprocessing and analysis 210 receives the PNGs and shapefiles, andoutputs vector data and shapefiles.

As shown in FIG. 2, the output of image processing and analysis 210 isinput to data integration 250. Also input to data integration 250 areorchard data 220, weather data 230 and soil type data 240. In general,data integration 250 merges the processed satellite data withproprietary orchard data, such as yield and quality data (orchard data220), as well as merging the data with soil types (soil type data 240)and weather conditions (weather data 230). Data integration 250processes the data from the different data sources to generate yield,quality, and geo-coordinates for the lots. With regard to the format,data integration 250 receives, as input, shapefiles, external API data,internal database data, and generates, as outputs, vector data inshapefiles.

As shown in FIG. 2, the output of data integration 250 is input to cropmanagement processing 260. In general, crop management processing 260implements a model to interpret the processed data and to identifyspecific problems. In some embodiments, the crop management processing260 uses twelve layers of geo-referenced data to identify problems withactionable recommendations. In some embodiments, crop managementprocessing 260 receives, as input, vector data, shapefiles and PNGs, andoutputs, vector data. In some embodiments, the crop managementprocessing 260 produces discrete, concrete recommendations, in the formof reports 270, to resolve farming anomalies based on the assessment andconclusions of processing the algorithms. For example, the cropmanagement processing 260 generates reports that recommend water programchanges, specific soil amendment recommendations, foliar nutrienttreatment options and farming practice scheduling changes.

Data & Data Sources

FIG. 3a illustrates the content of an example of Orchard data 220. Forthis example, Orchard data 220 is organized into individual lots.Although the system is described in conjunction with crop management forindividual lots, any field size, such as acreage or entire farms, may beused without deviating from the spirit or scope of the invention. Forthe example shown in FIG. 3a , each lot (e.g., Lots 1 & 2) hascorresponding data for “boundary coordinates”, “historical yield”,“historical quality”, “tree age/tree type” and “insect/disease history.”The Orchard data 220 is stored in electronic form for easy retrieval bya processing unit (processor and memory). For example, the Orchard data220 may be stored in a relational database.

FIG. 3b illustrates a block diagram to generate historical data forweather data (330) and water availability/quality data (340). Asdiscussed above, weather data, compiled by an external source (e.g.,NOAA), is retrieved from database 230. The raw weather data is used togenerate one or more chilling models (block 320) described above. Thechilling data may include accumulated chilling period, growing and chillportion data. The chilling data is organized by lot and stored for usein the crop management system. As shown in FIG. 3b , raw weather data isalso used to generate historical water data (quality and availability),on a lot-by-lot basis, and is stored for subsequent retrieval.

Orchard Data

In some embodiments, the precision agriculture remote sensing platformanalyzes orchards on a lot-by-lot basis. In other embodiments, theplatform may analyze entire farms, ranches or orchards, or any segmenttherein, such as by acreage or by specific orchard. In order to conductthe analysis, the precision agriculture remote sensing platform acquiresGID coordinates. The GID coordinates specify the boundaries of a lot. Ingeneral, the GID coordinates specify any shape of orchard or lot, suchas lots defined by any polygonal shape. In some embodiments, theprecision agriculture remote sensing platform provides a lot-by-lotidentification (ID). For example, the platform may produce maps oforchard and farms with lines delineating the orchard or farm into lots(i.e., the lots for analysis of the system). In some embodiments, theprecision agriculture remote sensing platform calculates acreages foreach lot based on the known boundaries of the lot. The orchard acreagemay be plotted to show acres by lot.

In some embodiments, orchard data includes a variable and data for treeage. The phenotypic expression of a tree is dependent upon the age ofthe tree. Because of this, orchard data incorporates tree age tointerpret the physical characteristics of the tree to ascertain theoutput conditions. As described more fully below, tree age is brokeninto buckets such that a bucket is analyzed using the same criteria.

In some embodiments, orchard data further includes the variable and datafor tree density. The tree density variable specifies the number oftrees per area of orchard. Tree density has an effect on phenotypicexpressions. As such, interpreting data of the physical characteristicsof the trees is aided by knowledge of tree density. In one embodimentdescribed more fully below, tree density is divided into four bucketsfor analysis.

Crop Quality Factors:

The precision agriculture remote sensing platform utilizes historicalquality data that measures crop quality over some historical period. Insome embodiments, the platform creates a radar chart that provides avisual pattern of historical crop quality across multiple parametersover several crop years. For example, to measure the quality ofpistachios, the quality parameter measures quality of the harvested cropin terms of trash, blank shells, insects and dark stains on the crop.FIGS. 4a-4f depict six radar charts, one for each lot (lot 120 in FIG.4a , lot 121 in FIG. 4b , lot 122 in FIG. 4c , lot 123 in FIG. 4d , lot124 in FIG. 4e and lot 125 in FIG. 4f ) that show quality data for“Insect”, “Trash”, “Blanks”, “Adhering” and “Dark Stain”, across 4 cropyears (i.e., 2012-2015).

In some embodiments, the precision agriculture remote sensing platformplots, by lot, orchard quality. This is known as a choropleth map. FIGS.5a-5f depict lot by various quality values. For this example, thequality factors of trash, insect, dark stain, blanks, closed mouth andadhering hull are shown. The percentages of the quality factor are shownfor each lot.

In some embodiments, the precision agriculture remote sensing platformconducts tree age cohort analysis. For this analysis, trees areseparated into individual categories or buckets based on age. Forexample, in some embodiments, the trees are separated into three or fourcohort tree age groups. FIGS. 6a-6b illustrate charts that depictquality items of pistachios tree data, including “blanks”, “insect”,“stain”, and “yield” in the vertical portion of the graph, plottedagainst crop year in the horizontal axis. As shown in FIGS. 6a-6b , eachplot corresponds to a cohort group classified by tree age (i.e., 11 to25, 26 to 50, 6 to 10, and greater than 50).

Yield by Lot:

In some embodiments, the precision agriculture remote sensing platformdetermines yield of the crop. Some permanent crops, such as pistachios,are alternate bearing crops with yields that vary from year to year(i.e., one high yield year followed by a low yield year). As such,historical crop yield is an important input to interpret sensed data.FIG. 7 shows a graph that depicts yield by lot for several exampleyears.

FIG. 7 depicts, for an example orchard plot, soil type relative to thepercent of closed mouth vegetation. This example shows, for loamy andmixed soil types, various percentages of trees with closed mouthvegetation.

In some embodiments, the orchard data includes data for phenology. Thephonology variable identifies the phase of the tree as signified by theseason (e.g., spring, summer, fall and winter). Of course, thephenotypic expressions of the tree will very throughout the seasons. Assuch, the phonology variable provides context when interpreting the dataon the physical characteristics of the trees.

The orchard data further includes, as a variable, alternate-bearingfactor. Certain trees, including pistachio trees, alter their productionand yield of fruit from year to year. A tree that produces lots of fruitone year is expected to produce much less fruit the following year. Thealternate-bearing factor is an important variable to interpretcharacteristics of permanent crops, such as trees, when assessingpotential problems with the tree.

External Data

In some embodiments, external data (i.e., not proprietary to theorchard) is used to identify output conditions. One type of externaldata is weather data. In some embodiments, the precision agricultureremote sensing platform uses the weather data as an important input tointerpret the conditions of an orchard. Weather data may include boththe variables “chilling hours” and “rainfall.” The rainfall variableindicates the amount of annual rainfall received in an orchard.Temperatures, used to create chilling data, may be collected fromweather stations. This temperature data is used to create chillingmodels that depict a wide range of temperatures a tree has been exposedto during the growing season. Various models may be used to createchilling data, such as “Growing Degree Hours”, “Utah Model”, “DynamicModel” and “Crossa-Raynaud model.” The historical chilling data providedata for both chill portions and chill hours. As described more fullybelow, in some embodiments, the chilling hours is broken into fourbuckets for analysis.

In some embodiments, the precision agriculture management system uses,as a type of external data, soil data. Soil data may include both soiltype as well as a characterization of soil drainage. For example, in oneembodiment, soil data includes the soil types of “Sandy”, “Loamy”, and“Clay.” Soil drainage may be characterized as “well drained.”

Satellite Data

Returning back FIG. 3c , a block diagram illustrating one embodiment forsatellite image processing is shown. For this embodiment, imageprocessing 350 receives, as input, satellite spectral data, andgenerates, as output, a number of spectral indices. Specifically, forthis embodiment, image processing 350 generates spectral indices for:normalized difference vegetation index (NDVI), optimized soil adjustedvegetation index (OSAVI), red green ratio index (RGRI), a greennormalized difference vegetation index (GNDVI) and a leaf area index(LAI).

Spectral Indices

In one embodiment, the precision agriculture remote sensing platformreceives, as input, spectral data (i.e., data generated from reflectionsof light from the terrain as measured across several spectral bands). Insome embodiments, the platform generates several spectral indices. Onespectral index is the normalized difference vegetation index (NDVI). TheNDVI is a widely used vegetative index in the field of remoteagriculture sensing. The NDVI provides a measure of healthy, greenvegetation. The index has a sensitivity that allows identification ofchlorophyll pigment, so as to provide a useful tool over a broad rangeof crops and conditions. In general, the NDVI reflects the contrastbetween the maximum absorption in the red, due to chlorophyll pigments,and the maximum reflection in the infrared band caused by leaf cellularstructure. Using the satellite spectral data, the NDVI may be calculatedas:

${NDVI} = \frac{\left( {{NIR} - {Red}} \right)}{\left( {{NIR} + {Red}} \right)}$

In some embodiments, the precision agriculture remote sensing platformutilizes an optimized soil adjusted vegetation index (OSAVI). The OSAVIprovides for greater soil variation than soil adjusted vegetation index(SAVI) for low vegetation cover, while demonstrating increasedsensitivity to vegetation cover greater than 50%. The OSAVI is best usedin areas with relatively sparse vegetation or when soil is visiblethrough the canopy. The OSAVI may be calculated, from the satellitespectral data, using the relationship:

${OSAVI} = \frac{1.5*\left( {{NIR} - {Red}} \right)}{\left( {{NIR} + {Red} + 0.16} \right)}$

In some embodiments, the precision agriculture remote sensing platformgenerates a red green ratio index (RGRI). The RGRI provides a ratioexpression of leaf redness, caused by anthocyanin, to that ofchlorophyll. The RGRI may be used to estimate the course of foliagedevelopment in canopies. It is an indicator of leaf production in stressand may also indicate flowering in some canopies. The RGRI may becalculated from the following expression:

${RGRI} = \frac{\sum\limits_{i = 600}^{699}\; R_{i}}{\sum\limits_{i = 500}^{599}\; R_{j}}$

In some embodiments, the precision agriculture remote sensing platformgenerates, from the satellite spectral data, a green normalizeddifference vegetation index (GNDVI). Similar to the NDVI, the GNDVImeasures the given spectrum from 540 to 570 nanometers, instead of thered spectrum, resulting in greater sensitivity to chlorophyllconcentration, than NDVI. The GNDVI may be expressed as:

$\frac{{\sum\limits_{j = 1}^{n}\; {W_{i,j}X_{j}}} - {\overset{\_}{X}{\sum\limits_{j = 1}^{n}\; W_{i,j}}}}{\sqrt[s]{\frac{\left\lbrack {{n{\sum\limits_{j = 1}^{n}\; W_{i,j}^{2}}} - \left( {\sum\limits_{j = 1}^{a}\; W_{i,j}} \right)^{2}} \right\rbrack}{n - 1}}}$

In some embodiments, the precision agriculture remote sensing platformfurther generates a leaf area index (LAI). The LAI may be used toestimate foliar coverage and to forecast crop growth and yield. Theexposed area of a leaf to sunlight plays a key role inevapotranspiration. Using the LAI to identify areas of weakness helpselucidate developmental problems as this index defines the area of thecrop that intersects with solar radiation. The LAI may be calculated as:

LAI=(3.618*EVI−0.118)>0

Variables

FIG. 8 illustrates one embodiment for some data structures used todefine orchard data, satellite data and weather data. For this exampleembodiment, the partial list of orchard data includes the variables of“tree age”, “tree density”, “soil data”, “quality factor”, “yield”,“alternate-bearing factor”, and “phenology.” The column next to thevariable name specifies the variable or data type. As shown in FIG. 8,the data type includes both categorical and numerical data types. Forexample, for the variable “tree age”, the data type is “categorical” asthe tree age is broken into four different categories (e.g., 0-5, 6-10,11-25 and greater than 25). The fourth column shown in FIG. 8 identifiesthe source of the data, such as proprietary (from the orchard owner),public data (USDA) and satellite data. The last column shows a briefexplanation of how the data is evaluated, including using amachine-learned model that parses the data into buckets for analysis ofsubsequent variables. For this embodiment, the satellite data includesthe variables “thermal image” and “vegetative index.” The weather dataincludes, for this embodiment, “chilling hours” and “rainfall.”Phenology is one variable with a categorical data type. For thisvariable, the categories include spring, summer, fall and winter.

FIG. 9 is a block diagram illustrating one embodiment for evaluatingvariables in a precision agriculture system to determine outputconditions. In some embodiments, the variables are processedsequentially (i.e., one variable at a time). As discussed above, datavalues, associated with a variable, are partitioned into buckets.Subsequently, the next variable is then analyzed on the bucket assignedfrom the previous variable. FIG. 9 represents one embodiment for anorder or priority to process the variables for evaluating orchard andsatellite data. For this embodiment, the precision agriculture systemevaluates, in this order, “tree age” (1010), “tree density” (1015),“soil type” (1020), “soil drainage” (1030), “phenology phase” (1040),“alternate-bearing cycle/yield factor” (1050), “heat unit/chillinghours” (1060), “crop quality data” (1070) and “yield” (1080). Inaddition to the variables for orchard data (and external weather data),the variables for satellite imagery are shown in the last stage of theevaluation. Specifically, satellite imagery is analyzed to determine thevariables “thermal”, “PAN”, and “multispectral” (1090).

Orchard Data Model

In some embodiments, the orchard data model consists of a hierarchy ofvariables which, when delineated into buckets, form pathways thatprovide correlations among the variables to one or more output metrics.FIG. 10 is a block diagram illustrating a process to create the orcharddata model in accordance with one embodiment. As discussed in FIG. 8,the variables for the orchard data model have several different datatypes. For example, in one embodiment, the data types include“categorical” and “numerical” for both orchard and external data. Forthe example shown in FIG. 10, the first variable, variable₍₁₎, has anumeric range from “0 to Z.” Using a machine learning process, during afirst stage of discovery, boundaries of values for the variables aredelineated to create buckets or pathways for optimizing the predictionsof the output conditions. As illustrated in FIG. 10, using machinelearning discovery, variable₍₁₎ is demarcated into three buckets: 0 to⅓Z, ⅓Z to ⅔Z, and ⅔Z to Z. Specific values for numeric ranges used invariables are discussed more fully below in conjunction with FIGS. 13(a)and 13(b).

For the example of FIG. 10, a second variable, variable₍₂₎, has acategorical data type (categories A, B, C, D . . . ). Similar tovariable₍₁₎, machine learning techniques are used to discover boundariesof variable₍₂₎ that demarcate buckets or pathways based on the differentcategories of the variable. This process continues for all of thevariables (up to variable_((n))) to discover pathways and create bucketsin order to develop a first stage model for the orchard data model(1140). Specifically, the discovery process determines how the values ofthe variable influence the output predictor (e.g., anomaly predictor).For example, in one embodiment, machine learning determines that theboundaries for the values for the tree age variable are (0-5), (6-10),(11-25), and (greater than 25). This demarcation for tree age revealsthat trees in these age brackets may be analyzed similarly (i.e., newtrees aged 0 to 5 have similar age related characteristics for analysisin the model). The chilling hours variable, also with a numerical datatype, was discovered to have an effective delineation of buckets thatconsist of less than 800, 801-900, 900-1,000 and greater than 1,000.

FIG. 11 is a flow diagram illustrating one embodiment for discoveringboundaries and variables for creating pathways or buckets in an orcharddata model. Data from one or more orchards are aggregated for multiplevariables (e.g., tree age, tree density, soil, chilling hours, etc.)(Block 1210). Output metrics are collected for one or more predictors(block 1220). The output metrics comprise observed conditions thatrelate to the output conditions (i.e., output conditions predicted bythe model). For example, for the output condition “disease detected”,the output metrics may include characteristics, observed on the trees,fruit or soil, associated with known disease problems, such as wilt. Tocreate a first stage for the orchard data model, machine-learningtechniques are run to discover boundaries of the variables based on thecorrelations of the variable values to the output metrics for thepredictors (block 1230). For example, for the “disease detected” outputpredictor, the first stage training phase correlates ranges within thevariables, such as tree density ranges, based on the output metrics(e.g., diseased orchards). Any machine-learning techniques, such asmachine learning techniques that use various types of machine-learningalgorithms (e.g., nonlinear regression, linear regression, etc.) may beused to establish the relationships between the input variables and theoutput metrics without deviating from the spirit and scope of theinvention.

FIG. 12 is a block diagram illustrating one embodiment for generatingthe orchard data model through a second machine learning stage. Acomputer platform is used to create the orchard data model. Orchard datamodel 1310 includes a number of variables, each variable is partitionedinto buckets. For the specific embodiment of FIG. 12, the variables oftree age, tree density, soil, chilling hours, and quality arepartitioned into preliminary pathways discovered in the first machinelearning stage. The lines shown in FIG. 12 connect the buckets inorchard data model 1310 to create exemplary pathways discovered in thefirst machine learning stage. The paths shown in FIG. 12 are onlyillustrative of a concept, such as the concept of learned pathwaysthrough nodes of a neural network, and are not to be construed as actualpaths discovered in an implementation. The orchard data model 1310 isrun against a training model (per orchard) 1320. During the secondmachine learning stage, the layers of variables are further correlatedto the output metrics 1330. In essence, the output metrics 1330calibrate the orchard data model to the output conditions. In someembodiments, the output metrics 1330 may be an observed condition at theorchard, such as the observed conditions of disease, water leak, soilproblems, etc. Since the data for the variables and the output metricsare specific to an orchard, the second machine learning stage builds anorchard data model specific to each orchard (i.e., each orchard has aunique signature due to its unique characteristics). Anymachine-learning techniques, such as machine learning techniques thatuse various types of machine-learning algorithms (e.g., nonlinearregression, linear regression, etc.) may be used to establish therelationships between the buckets of input variables and the outputmetrics for the second machine learning stage without deviating from thespirit and scope of the invention.

FIGS. 13a and 13b are block diagrams illustrating one embodiment for atrained orchard data model. For this embodiment, several variables aretrained to assess the output conditions in orchard data model 1400.Specifically, the variables of tree age, tree density, soil type, soildrainage, phenology cycle, alternate bearing factor, heat units/chillinghours, crop quality data and yield are used to predict the outputconditions of water leak, salt damage, generic anomaly, soil problem anddisease detected. As discussed above, during the first machine learningstage, the machine-learning techniques discover the boundaries for thevariables to demarcate the variables into buckets. For the example ofFIG. 13a , tree age was divided into the buckets of 0 to 5 years, 6-10years, 11-25 years and greater than 25 years. For this example, thesecond variable, tree density, was partitioned into three buckets: lessthan 125 trees per acre, 126-145 trees per acre, and greater than 145trees per acre. The soil type variable, a categorical data type, isclassified as “sandy”, “loam”, and “clay.” The fourth variable shown onFIG. 13a , soil drainage, includes, for this embodiment, the categories“poor”, “average” and well-drained.” The fifth variable, which definesthe phenology cycle of the trees, comprises the categories of “winter”,“spring”, “summer” and “fall.”

The alternate bearing factor is the sixth variable shown in FIG. 13b .The alternate bearing factor is a measure of how much energy the treeexerted in the past as evidenced by the crop output. The alternatebearing factor, for this embodiment, includes the categories of “low”,“medium” and “high.” The seventh variable, heat unit/chilling hours,includes the relevant boundaries of less than 800 hours, 801-1000 hours,and greater than 1000 hours. The eighth variable shown in FIG. 13b iscrop quality data (i.e., quality of the fruit produced). For thisexample, the crop quality data variable includes a data structure thatidentifies both a quality metric (insect, stained, and closed mouth) aswell as a quantitative assessment of each quality indicator (e.g., highor low). The last variable shown in the embodiment of FIG. 13b is theyield variable, expressed in pounds per acre, that specifies ranges ofyields for orchards. Specifically, for this embodiment, the ranges are“0-1K”, “1K-2K”, “2K-3K”, “3K-4K.”

The orchard data model 1400, shown in FIG. 13a and FIG. 13b , include anumber of decision elements (i.e., 1411, 1412, 1413, 1414, 1421, 1422,1423, 1424, 1431, 1432, 1433, 1434, 1441, 1442, 1443 1444, 1451, 1452,1453, 1454, 1461, 1462, 1463, 1464, 1471, 1472, 1473, 1474, 1481, 1482,1483, 1484 and 1490). These decision elements create the pathways of themodel (learned pathways) based on the orchard data specific to theorchard. For this embodiment, the orchard data model 1400 generatespredictions for the output conditions of healthy vegetation, saltdamage, soil problem, generic anomalies and disease detected.

Satellite Data Model

FIG. 14(a) is a block diagram illustrating one embodiment to generate asatellite model for integration into a precision agriculture system. Forthis embodiment, a two-step process is used. First, satellite imageinformation is collected and spectral indices are calculated. Then, thesatellite images are analyzed to detect spectrum(s) of frequencies andto interpret the spectral indices to detect one or more outputconditions. In one embodiment, a radio spectrometry system (1510) isused to gather reflectance information for one or more attributes orqualities of the orchard (1500). Specifically, radio spectrometryequipment 1510 is co-located, such as at orchard 1500. Then,characteristics of one or more attributes or qualities of the orchard(1500), such as standing water, are identified. The radio spectrometryequipment 1510 is used to transmit and then gather reflected bands offrequencies that capture the desired attributes or qualities of theorchard (1500). For example, if the attribute is soil (e.g.,differentiate soil from trees), then the radio spectrometry 1510identifies a band of frequencies that identify soil only. A descriptionof various characteristics, attributes or qualities observed andrecorded in the orchards is described more fully below in conjunctionwith FIGS. 15(a)-(e).

FIG. 14(b) depicts a block diagram for processing satellite imagery toidentify characteristics through previously identified bands offrequencies discovered from the process depicted in FIG. 14(a). For thisexample, satellite imagery (1530) is input to a processing block (1540)to evaluate and track the bands of frequencies discovered in the fieldcalibration experiments. A computer platform is used to evaluate theband of frequencies 1540. For this example, the precision agriculturesystem evaluates frequencies to detect disease, soil, salt damage, waterleak, generic anomalies and healthy vegetation.

FIG. 15(a) depicts using field equipment to gather bands of wavelengthsassociated with disease crops. For this application, orchard 1612includes samples and examples of diseased trees (1620 and 1630). Thedisease of the tree may be identified as a particular known condition,such as Verticillium Wilt 1640, or have an appearance of a general butnon-identified disease condition. The radio spectrometer 1608 transmitsenergy to the example diseased trees, and records the bands ofreflectance for the various disease conditions as disease bands ofwavelengths 1610.

FIG. 16 depicts a satellite image for an example orchard lot thathighlights possible Verticillium Wilt reflectance regions. As shown inFIG. 16, the correlation of the bands of frequencies in the satelliteimage to the healthy vegetation reflectance regions are plotted,including how the satellite image for the orchard compares with diseasereflectance regions. The different gray scales shown correlate to theSMA_Mean chart to show the level of comparison between the satelliteimage and the detected Verticillium Wilt reflectance regions.

FIG. 15(b) depicts a process to generate healthy bands of wavelengthsreflected from healthy orchards. Orchard 1648 has multiple samples ofhealthy trees (1650, 1655 and 1660). Trees may be characterized ashealthy trees from multiple characteristics or qualities of the tree,including quality of the crop and appearance characteristics of the treefor known healthy characteristics. Radio spectrometry 1643 emits energyto orchard 1648, captures reflectance bands for conditions associatedwith the sample healthy trees to create the healthy bands of wavelengths1645.

FIG. 17 depicts a satellite image for an example orchard lot thathighlights healthy vegetation reflectance regions. As shown in FIG. 17,the correlation of the bands of frequencies in the satellite image tothe healthy vegetation reflectance regions are plotted, including howthe satellite image for the orchard compares with healthy vegetationreflectance regions. The different gray scales shown correlate to theSMA_Mean chart to show the level of comparison between the satelliteimage and the detected healthy vegetation reflectance regions.

FIG. 15(c) illustrates a process to collect soil bands of wavelengthsfor use in the precision agriculture system. For this process, severalsoil conditions adversely affecting the orchards are identified. Radiospectrometry 1663, co-located with orchard 1668, transmits energy, andrecords the soil bands of wavelengths 1670 by associating thereflectance bands of wavelengths with soil conditions. The bands ofwavelengths may include a band of wavelengths used to specificallyidentify salt damage (1665). The soil condition may be a specificcondition, such as salt damage 1676, or it may be a generalizedcondition identified as a soil anomaly (1672 and 1674).

FIG. 18 depicts a satellite image for an example orchard lot thathighlights possible salt damaged reflectance regions. As shown in FIG.18, the correlation of the bands of frequencies in the satellite imageto the possible salt damaged reflectance regions are plotted, includinghow the satellite image for the orchard compares with possible saltdamaged reflectance regions. The different gray scales shown correlateto the SMA_Mean chart to show the level of comparison between thesatellite image and the detected possible salt damaged reflectanceregions.

FIG. 19 depicts a satellite image for an example orchard lot thathighlights soil reflectance regions. As shown in FIG. 19, thecorrelation of the bands of frequencies in the satellite image to thesoil reflectance regions are plotted, including how the satellite imagefor the orchard compares with soil reflectance regions. The differentgray scales shown correlate to the SMA_Mean chart to show the level ofcomparison between the satellite image and the soil reflectance regions.

FIG. 15(d) illustrates a field calibration process to acquire a band offrequencies that detect water leak. To calibrate the water leak outputcondition, standing water (1685) is identified in orchard 1682. Radiospectrometry 16788 emits the energy and records the reflectance of bandsof frequencies that identify the standing water to generate the waterleak band of frequencies 1680.

FIG. 15(e) depicts a process to generate bands of frequencies thatidentify generic anomalies in orchards. To calibrate these bands offrequencies for use in the precision agriculture system, unhealthy treesare identified in orchard 1690. An unhealthy tree is a tree thatexhibits any number of characteristics that indicate poor health. Theobserved characteristic may be identifiable as having a root cause orthe characteristic may generally reflect a tree in poor health withoutan identified cause. Radio spectrometry 1687, co-located with orchard1690, transmits energy and captures the reflectance of bands offrequencies associated with the observed characteristics in unhealthytrees to generate bands of frequencies for generic anomalies 1692.

Generic anomalies may also be calculated from spectral indices. In someembodiments, the precision agriculture system calculates leaf area index(LAI) to determine the surface area of the leaf exposed upward, andtherefore the angle of the leaf relative to the sun in the sky above.The leaves of a healthy tree will track the movement of the sun as thesun moves across the sky during the day. The precision agriculturesystem samples the LAI several times during the day in order to trackthe orientation of the leaf to the sky. In some embodiments, theprecision agriculture system compares LAIs from different tress todetermine whether the movement of the leaves is fairly consistent amongthe tress. If any trees do not possess LAIs as other tress in theorchard, then those trees are flagged as a generic anomaly.

FIG. 20 illustrates a system for generating a satellite image model inaccordance with one embodiment for the precision agriculture system ofthe present invention. As discussed above, satellite images are received(satellite imagery acquisition 2110) for intermediate processing insatellite image processing (2120) to generate spectral indices. Thespectral indices are input to the satellite imagery data model to detectvarious output conditions (e.g., water leak, healthy vegetation, soildetection, generic anomalies, disease detection, etc.). Also, asdiscussed above, bands of frequencies, obtained from the field studies,are determined to detect the various output conditions. Satelliteimagery data model 2130 receives, as inputs, processed satellite imagesfrom satellite imagery processing 2120 and the bands of frequencies forthe output conditions (2150). The satellite imagery data model istrained to predict the output conditions from the processed satelliteimagery based on spectral indices, bands of frequencies (2150) and theoutput metrics (2160). As shown in FIG. 20, for this embodiment,machine-learning module (2140) uses a machine-learning algorithm (e.g.,nonlinear regression, linear regression, etc.) to correlate theprocessed satellite imagery (spectral indices) and the bands offrequencies (2150) to the output metrics (2160). The machine-learningmodule (2140) also validates, and calibrates as necessary, paths todetermine the output conditions from the band of frequencies (radiospectrometry data) with paths that interpret the spectral indices todetermine output conditions. The output metrics provide the observedcharacteristics at the orchard that correspond to the output conditions(e.g., water leak, healthy vegetation, soil detection, genericanomalies, disease detection, etc.). As is recognized in the art ofcomputing, various training techniques, such as training techniques thatuse training data sets, and machine learning algorithms (e.g., nonlinearregression, linear regression, etc.) may be used to train the satelliteimagery data model to extract the bands of frequencies from theprocessed satellite imagery to predict one or more output conditions,without deviating from the spirit or scope of the invention.

FIG. 21 is a block diagram illustrating a process to identify, fromsatellite images, soil images separate from tree vegetation images. Asdiscussed herein, soil band reflectance data is collected, such asacquired through a field test using spectrometry, as a reference todifferentiate tree vegetation from soil in satellite imagery. Satelliteimagery 2210 and soil band reflectance data 2220 are input to aprocessing platform, labeled spectral angle mapper 2230 in FIG. 21. Ingeneral, the spectral angle mapper 2230 analyzes the satellite imagesand uses the soil band reflectance data to classify the pixels of thesatellite imagery as either “soil” or “tree vegetation.” Block 2240 onFIG. 21 represents the output of the classifier (i.e., only the soilportion of the orchard from the satellite imagery). Although the processis described using a spectral angle mapping technique, any classifieralgorithm may be used to differentiate between the soil and treevegetation without deviating from the spirit and scope of the invention.

Model Integration

FIG. 22 is a block diagram illustrating one embodiment for integratingan orchard data model and a satellite image model into the precisionagriculture machine learned model of the present invention. In someembodiments, orchard data model (2310) and satellite image model (2320)may operate as independent predictors for the output conditions. Assuch, a precision agriculture system that integrates both modelsprovides two prediction mechanisms and, when properly integratedtogether, enhance the overall predictability of the precisionagriculture system. Although the precision agriculture machine learnedmodel is described as combining the orchard data model and the satelliteimage model, any one, or any combination of both models, may be used topredict one or more output conditions without deviating from the spiritand scope of the invention.

As shown in FIG. 22, orchard data model (2310), satellite image model(2320) and anomaly predictors (2350) (i.e., output conditions) are inputto machine-learning techniques processing block (2330). In turn,machine-learning techniques module (2330) executes a learning model togenerate precision agriculture machine learned model (2340) bycorrelating the individual predictions of the orchard data model (2310)and the satellite image model (2320) to the output metrics for theanomaly predictors. Any machine-learning techniques, such as machinelearning techniques that use various types of machine-learningalgorithms (e.g., nonlinear regression, linear regression, etc.) may beused to establish the relationships among the individual predictions ofthe orchard data model (2310) and the satellite image model (2320) tothe output metrics for the anomaly predictors without deviating from thespirit and scope of the invention. In general, the output metrics mayinclude any type of ground truth data used to validate or calibrate themodel(s). For example, output metrics may comprise observedcharacteristics at an orchard that are known to the farm managers tocorrelate to specific output conditions. The output metrics may alsocomprise ground truth data, generated by examining the orchards forwhich output conditions are predicted and determining the accuracy ofthe prediction. Embodiments for entering observed characteristics asoutput metrics into a computer platform for integration into theprecision agriculture model are described more fully below. In someembodiments, the orchard data model (2310) and the satellite image model(2320) are integrated and calibrated on an orchard-by-orchard basis(i.e., a unique precision agriculture machine learned model for eachorchard). As such, the techniques illustrated in FIG. 22 calibrate theorchard data model and satellite image model for use in predictingoutput conditions in a specific orchard.

FIG. 23 is a block diagram illustrating a precision agriculture systemthat incorporates a means to input and integrate farming knowledge(e.g., output metrics). In some embodiments, the precision agriculturesystem includes a farming knowledge module (3010) that receivesinformation to integrate agricultural knowledge of one or more orchards,including experience that certain observation of characteristics lead tospecific output conditions. Through farming knowledge module (3010),specific farming know-how, developed over a period of time in farmingorchards, may be captured, saved and subsequently used in the precisionagriculture system. In general, the farming knowledge module (3010),through the user interface (3020), allows a user to input data tocharacterize know-how related to farming practices for the orchards. Insome embodiments, one way for the user to enter farming know-how andobserved characteristics as the output metrics is to enter one or moreof those characteristics of the orchards, as well as to enter one ormore output conditions generally known to follow from the observedcharacteristics. For example, a farmer may analyze leaf pigmentation intrees, such as red leaf pigmentation, and may have the know-how toidentify stress from the leaf pigmentation as a consequence of disease.For this example, the user may input, through the user interface screen(3030), a list of observed conditions for the tress, such as leaf colorobservations, observations about soil conditions, water, climate, otherphenotypic data about the crop. Examples of observed characteristics(e.g., red pigmentation, chlorophyll, leaf senescence, nitrogen levelsof soil, observed water conditions, etc.) are shown in the left columnin FIG. 23. In addition, the user may enter an output conditionassociated with the observed conditions, such as disease, as shown onthe right hand column of FIG. 23. Similarly, the user, through use ofthe user interface (3020), may input any observed characteristicspotentially affecting the orchards (e.g., tree age, tree density, soiltype, soil drainage, phenology phase, yield factor, chill hours, cropquality data, etc.) and a corresponding observed output condition (e.g.,disease, soil problem, generic anomalies, salt damage, and healthybands, water problems, etc.), without deviating from the spirit of scopeof the invention.

As shown in FIG. 23, the captured observed characteristics and outputconditions are processed in the farming knowledge module (3010) andsubsequently stored in a database, such as a relational database, on aphysical storage medium (3040). This process, which includes capturingobserved characteristics and corresponding output conditions, serves asa means for the precision agriculture system to capture farmingexpertise, including valuable farming experience and knowledge aboutorchards, into a database. As described herein, the farming expertisestored in the farming knowledge data store (3040) is used, as groundtruth data, to qualify, interpret and train the precision agriculturelearned model(s), including used as output metrics to train the orcharddata model and the satellite data model.

FIG. 24 is a block diagram illustrating one embodiment for using farmingknowledge to interpret the output of the precision agriculture learnedmodel. For this environment, the precision agriculture learned model(3110) predicts various output conditions, such as disease, soilproblem, generic anomalies, salt damage, and healthy bands, waterproblems, etc.), labeled output conditions 3120. For some embodiments,the output conditions are represented as a range of values, expressed asSMA, that indicate the intensity or strength of the output conditionprediction as it correlates to the orchards under analysis. The range ofoutputs for an output condition is illustrated as a range of SMA values(3130) in FIG. 24. The farming knowledge database (3150) storesinformation that correlates one or more observed characteristics of theorchards to one or more output conditions, as described above. In someembodiments, these correlations are used as linear constraints wheninterpreting a predicted output conditions from the precisionagriculture learned model. As shown in FIG. 24, a range of SMA valuesfor a predicted output condition is input to interpretation module(3140). In general, the interpretation module (3140) applies constraintsto the predicted output conditions so as to interpret the condition.From this process, the interpretation module (3140) generates buckets ofcategories (3160) for a predicted output condition, illustrated asbuckets (3170) and (3180). For example, one bucket may signify, for atop range of SMA values, an extremely high probability that the outputcondition exists in the orchards under analysis, while a second bucketmay signify, for a second range of SMA values, a lower probability(e.g., 50%) that the output condition exists in the orchards underanalysis. In this way, the farming knowledge is integrated into theprecision agriculture system to increase the specificity of the outputin the ability of the precision agriculture model to predict the outputcondition in the orchards under analysis.

Precision Agriculture Model

FIG. 25 is a flow diagram illustrating one embodiment for integratingand calibrating a precision agriculture machine learned model. Asdiscussed above, in some embodiments, a precision agriculture machinelearned model is generated for each orchard plot to reflect the uniquecharacteristics of each orchard. To initiate the process, the orchardplot is selected to create the precision agriculture model. A computerplatform is used to create the precision agriculture model. The orcharddata model and the orchard data for the plot are loaded into thecomputer platform (blocks 2420 and 2430). Also, the satellite model andsatellite imagery are loaded for the orchard plot (block 2440).Machine-learning techniques, running on the computer platform, generatea precision agricultural machine learned model by correlating both theorchard data model and the satellite image model output predictors tothe output metrics for the orchard plot (block 2450).

FIG. 26 is a block diagram illustrating one embodiment for a precisionagriculture system. As shown in FIG. 26, outputs from precisionagriculture machine learned model (2500) are input to spatialautocorrelation processing (2510). In general, the spatialautocorrelation module (2510) processes the output predictions (e.g.,output conditions measured) to interpolate the data so as to developintensity values across the areas of the orchard plot. The interpolationof the data from the precision agriculture learned model allows the datato be mapped across an orchard plot to see “hotspots” for the outputprediction. For example, using spatial autocorrelation, the data ismapped to an orchard plot, and severity across the area of the map maybe visualized. These hotspots identify areas in the orchard with theoutput condition.

In some embodiments, the spatial autocorrelation module (2510) useslinear interpolation for generating autocorrelation statistics, such asa spatial statistics model (e.g., Getis-Ord). Although the precisionagriculture system discloses use of linear interpolation, anyinterpolation that generates intensity and severity for orchard plotsmay be used without deviating from the spirit or scope of the invention.As shown in FIG. 26, the output conditions with the spatial and densityare input to the output detection module (e.g., water leak 2525, healthyvegetation 2530, salt damage 2535, generic anomalies 2540, soil problems2545 and disease detection 2550).

FIG. 27 depicts an output from the hotspot detection module identifyingintensity of generic anomalies across an example orchard. The map iscoded in gray scale, ranging from dark regions, which identify theintensity of the generic anomalies, to the lighter regions that signifya lower intensity value for generic anomalies.

FIG. 28 depicts hotspot analysis based on an analysis of vegetativehealth. This output plots a range of vegetative health from veryunhealthy to very healthy. The index for the vegetative health is shownnext to the corresponding plot for an example orchard.

In general, the precision agriculture model runs data for orchards underanalysis to detect one or more output conditions. In some embodiments,the data, associated with the predicted output conditions, may be viewedat different resolutions across one ore more orchard lots underanalysis. For example, a graph for an output condition, such as a graphshowing generic anomalies, may be plotted to illustrate variousintensity levels or hot spots of generic anomalies across severalorchard lots, a single orchard, a cluster of trees within an orchard,and even a single tree within an orchard. As such, the precisionagriculture system permits a user to extract data ranging from an entireorchard down to a single tree.

FIG. 29 is a flow diagram illustrating one embodiment for a run timeoperation executed by the precision agriculture system. Orchard andsatellite data are retrieved for orchards under analysis (block 3210).Using the orchard and satellite data, as well as the precisionagriculture learned model for the orchards, the precision agriculturemodel is executed to predict whether one ore more output conditions,associated with the orchards under analysis, exist. In one or moreembodiments, the user may view data, on one or more output conditions,at various levels of detail or various resolutions of the orchards underanalysis. This feature is illustrated in FIG. 29 in decision block(3240) such that the system receives input from a user to view dataassociated with output conditions.

The user interface for the module may include any type of interface forthe precision agriculture system that receives user input to selectresolution of information regarding the output conditions. For example,the precision agriculture system may display a means for a user toselect a level of resolution of output conditions on a map. In this way,the user may select specific portions of the orchard to visualize orotherwise extract output condition data. For the embodiment illustratedin FIG. 29, the module displays predicted output conditions for anorchard at the location and resolution specified through the interface(block 3250). For example, if the user desires to extract data down to aspecific tree, then the user navigates to the display of the orchardunder analysis to select a specific tree to extract any outputconditions that exist. Similarly, the user may evaluate sections of anorchard, groups of specific trees, rows or columns of trees, etc. Themodule, shown in FIG. 29, is an iterative process as the system receivesadditional input from the user, and in turn, displays output conditionfor portions of the orchard specified in the request (blocks 3260, 3240and 3250).

Computer Platform

FIG. 30 depicts a diagrammatic representation of a machine in theexemplary form of a computer system 29A00 within which a set ofinstructions for causing the machine to perform any one of themethodologies discussed above may be executed. In alternativeembodiments, the machine may comprise a network router, a networkswitch, a network bridge, a personal digital assistant (PDA), a cellulartelephone, a web appliance, or any machine capable of executing asequence of instructions that specify actions to be taken by thatmachine.

The computer system 29A00 includes a CPU partition having one or moreprocessors (e.g., processor 2902 _(1,) processor 2902 ₂, etc.), a mainmemory comprising one or more main memory segments (e.g., main memorysegment 2904 ₁, main memory segment 2904 ₂, etc.), and one or morestatic memories (e.g., static memory 2906 ₁, static memory 2906 ₂,etc.), any of which components communicate with each other via a bus2908. The computer system 29A00 may further include one or more videodisplay units (e.g., display unit 2910 ₁, display unit 2910 ₂, etc.)such as an LED display, or a liquid crystal display (LCD), a cathode raytube (CRT), etc. The computer system 29A00 can also include one or moreinput devices (e.g., input device 2912 ₁, input device 2912 ₂,alphanumeric input device, keyboard, pointing device, mouse, etc.), oneor more database interfaces (e.g., database interface 2914 ₁, databaseinterface 2914 ₂, etc.), one or more disk drive units (e.g., drive unit2916 ₁, drive unit 2916 ₂, etc.), one or more signal generation devices(e.g., signal generation device 2918 ₁, signal generation device 2918 ₂,etc.), and one or more network interface devices (e.g., networkinterface device 2920 ₁, network interface device 2920 ₂, etc.).

The disk drive units can include one or more instances of amachine-readable medium 2924 on which is stored one or more instances ofa data table 2919 to store electronic information records. Themachine-readable medium 2924 can further store a set of instructions2926 ₀ (e.g., software) embodying any one, or all, of the methodologiesdescribed above.

A set of instructions 2926 ₁ can also be stored within the main memory(e.g., in main memory segment 2904 ₁). Further, a set of instructions2926 ₂ can also be stored within the one or more processors (e.g.,processor 2902 ₁). Such instructions and/or electronic information mayfurther be transmitted or received via the network interface devices atone or more network interface ports (e.g., network interface port 2923₁, network interface port 2923 ₂, etc.). Specifically, the networkinterface devices can communicate electronic information across anetwork using one or more optical links, Ethernet links, wireline links,wireless links, and/or other electronic communication links (e.g.,communication link 2922 ₁, communication link 2922 ₂, etc.). One or morenetwork protocol packets (e.g., network protocol packet 2921 ₁, networkprotocol packet 2921 ₂, etc.) can be used to hold the electronicinformation (e.g., electronic data records) for transmission across anelectronic communications network (e.g., network 2948). In someembodiments, the network 2948 may include, without limitation, the web(i.e., the Internet), one or more local area networks (LANs), one ormore wide area networks (WANs), one or more wireless networks, and/orone or more cellular networks.

The computer system 29A00 can be used to implement a client systemand/or a server system, and/or any portion of network infrastructure.

It is to be understood that various embodiments may be used as, or tosupport, software programs executed upon some form of processing core(such as the CPU of a computer) or otherwise implemented or realizedupon or within a machine or computer readable medium. A machine-readablemedium includes any mechanism for storing or transmitting information ina form readable by a machine (e.g., a computer). For example, amachine-readable medium includes read-only memory (ROM), random accessmemory (RAM), magnetic disk storage media, optical storage media, flashmemory devices, or any other type of non-transitory media suitable forstoring or transmitting information.

A module as used herein can be implemented using any mix of any portionsof the system memory, and any extent of hard-wired circuitry includinghard-wired circuitry embodied as one or more processors (e.g., processor2902 ₁, processor 2902 ₂, etc.).

What is claimed is:
 1. A computer-implemented method to detect at leastone output condition for precision agriculture management of permanentcrops, comprising: storing, in the computer platform, at least oneprocessed satellite image associated with a lot under analysis; storing,in the computer platform, at least one reflectance dataset comprising aband of frequencies, detectable from the processed satellite image,associated with one or more generic anomaly conditions, wherein thegeneric anomaly conditions simulate reflectance data received by asatellite for crops exhibiting generic anomalies; and processing thereflectance dataset and the processed satellite images in a precisionagriculture management model, operating on the computer platform, topredict a propensity for existence of the generic anomalies, wherein theprecision agriculture management model, through machine-learningtechniques, discovers associations of the generic anomalies from thereflectance dataset to the processed satellite image.
 2. Thecomputer-implemented method as set forth in claim 1, further comprising:analyzing the generic anomaly conditions to identify at least one hotspot area for the lot.
 3. The computer-implemented method as set forthin claim 2, wherein analyzing the generic anomaly conditions to identifyat least one hot spot area for the lot further comprises processing thesatellite image by applying spatial autocorrelation statistics toidentify the hot spot area.
 4. The computer-implemented method as setforth in claim 1, further comprising: conducting a field test at a lotwith crops exhibiting one or more unhealthy conditions by recording aband of frequencies for the reflectance dataset associated with at leastone generic anomaly condition using radio spectrometry so as tosimulate, with the band of frequencies, reflectance data received by asatellite for crops exhibiting the generic anomalies condition.
 5. Thecomputer-implemented method as set forth in claim 1, further comprising:determining, in the computer platform, a count of generic anomalies forthe lot; and discarding the existence of the generic anomalies if thecount of generic anomalies for the lot is high.
 6. Thecomputer-implemented method as set forth in claim 1, further comprising:determining, in the computer platform, a count of the generic anomaliesfor the lot; determining, in the computer platform, a severity of thegeneric anomalies for the lot; and processing a recommendation for thelot if the count of the generic anomalies is high and the severity ofthe generic anomalies is high.
 7. The computer-implemented method as setforth in claim 1, further comprising: determining, in the computerplatform, a count of the generic anomalies for the lot; and processing arecommendation for the lot if the count of the generic anomalies isaverage to high.
 8. A computer readable medium, embodied in anon-transitory computer readable medium, the non-transitory computerreadable medium having stored thereon a sequence of instructions which,when stored in memory and executed by a processor causes the processorto perform a set of acts, the acts comprising: storing, in the computerplatform, at least one processed satellite image associated with a lotunder analysis, storing, in the computer platform, at least onereflectance dataset comprising a band of frequencies, detectable fromthe processed satellite image, associated with one or more genericanomaly conditions, wherein the generic anomaly conditions simulatereflectance data received by a satellite for crops exhibiting genericanomalies; and processing the reflectance dataset and the processedsatellite images in a precision agriculture management model, operatingon the computer platform, to predict a propensity for existence of thegeneric anomalies, wherein the precision agriculture management model,through machine-learning techniques, discovers associations of thegeneric anomalies from the reflectance dataset to the processedsatellite image.
 9. The computer readable medium as set forth in claim8, further comprising: analyzing the generic anomaly conditions toidentify at least one hot spot area for the lot.
 10. The computerreadable medium as set forth in claim 9, wherein analyzing the genericanomaly conditions to identify at least one hot spot area for the lotfurther comprises processing the satellite image by applying spatialautocorrelation statistics to identify the hot spot area.
 11. Thecomputer readable medium as set forth in claim 8, further comprising:conducting a field test at a lot with crops exhibiting one or moreunhealthy conditions by recording a band of frequencies for thereflectance dataset associated with at least one generic anomalycondition using radio spectrometry so as to simulate, with the band offrequencies, reflectance data received by a satellite for cropsexhibiting the generic anomalies condition.
 12. The computer readablemedium as set forth in claim 8, further comprising: determining, in thecomputer platform, a count of generic anomalies for the lot; anddiscarding the existence of the generic anomalies if the count ofgeneric anomalies for the lot is high.
 13. The computer readable mediumas set forth in claim 8, further comprising: determining, in thecomputer platform, a count of the generic anomalies for the lot;determining, in the computer platform, a severity of the genericanomalies for the lot; and processing a recommendation for the lot ifthe count of the generic anomalies is high and the severity of thegeneric anomalies is high.
 14. The computer readable medium as set forthin claim 8, further comprising: determining, in the computer platform, acount of the generic anomalies for the lot; and processing arecommendation for the lot if the count of the generic anomalies isaverage to high.
 15. A system comprising: a storage medium, havingstored thereon, a sequence of instructions; at least one processor,coupled to the storage medium, that executes the instructions to causesthe processor to perform a set of acts comprising: storing, in thecomputer platform, at least one processed satellite image associatedwith a lot under analysis; storing, in the computer platform, at leastone reflectance dataset comprising a band of frequencies, detectablefrom the processed satellite image, associated with one or more genericanomaly conditions, wherein the generic anomaly conditions simulatereflectance data received by a satellite for crops exhibiting genericanomalies; and processing the reflectance dataset and the processedsatellite images in a precision agriculture management model, operatingon the computer platform, to predict a propensity for existence of thegeneric anomalies, wherein the precision agriculture management model,through machine-learning techniques, discovers associations of thegeneric anomalies from the reflectance dataset to the processedsatellite image.
 16. The system as set forth in claim 15, furthercomprising: analyzing the generic anomaly conditions to identify atleast one hot spot area for the lot.
 17. The system as set forth inclaim 16, wherein analyzing the generic anomaly conditions to identifyat least one hot spot area for the lot further comprises processing thesatellite image by applying spatial autocorrelation statistics toidentify the hot spot area.
 18. The system as set forth in claim 15,further comprising: conducting a field test at a lot with cropsexhibiting one or more unhealthy conditions by recording a band offrequencies for the reflectance dataset associated with at least onegeneric anomaly condition using radio spectrometry so as to simulate,with the band of frequencies, reflectance data received by a satellitefor crops exhibiting the generic anomalies condition.
 19. The system asset forth in claim 15, further comprising: determining, in the computerplatform, a count of generic anomalies for the lot; and discarding theexistence of the generic anomalies if the count of generic anomalies forthe lot is high.
 20. The system as set forth in claim 15, furthercomprising: determining, in the computer platform, a count of thegeneric anomalies for the lot; determining, in the computer platform, aseverity of the generic anomalies for the lot, and processing arecommendation for the lot if the count of the generic anomalies is highand the severity of the generic anomalies is high.